Abstract
The lack of spatial industrial output value data limited the risk and disaster assessment of industrial economy responding to global change. Therefore, we developed a new method to spatialize industrial output value coupling DMSP/OLS (Defense meteorological satellite program/operational linescan system) nighttime light data, MODIS (Moderate-resolution imaging spectroradiometer) annual vegetation data, industrial land distribution data and urbanization rate. A grid data set of 1 km industrial output value of China was created using this method. The main steps creating the data set were as follows: (1) data preprocessing and selecting stable lighting data; (2) constructing an Enhanced Vegetation Index (EVI) adjusted nighttime light index (EANTLI); (3) obtaining optimum light index by industrial land distribution data; (4) constructing spatial distribution model of industrial output value; (5) verifying data accuracy. We randomly selected 105 cities nationwide to assess the accuracy of the data set. The results show that the relative errors of whole samples ranged from 0% to 39.6%,the relative errors of most samples were less than 15%, and the average accuracy of the data set was as high as 81.40%. The dataset solved the problem that the industrial output value and service output value are difficult to be distinguished in value spatialization. The dataset broke the limits of administrative boundaries so as to directly reflect the spatial and temporal disparities and distribution features of industrial output value. The advances of the dataset could contribute to the identification of China’s key industrial distribution areas and discern the change trend of industry.
Published Version
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